diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
index 12d789410..4bfecb3a9 100644
--- a/website/docs/usage/spacy-101.md
+++ b/website/docs/usage/spacy-101.md
@@ -238,15 +238,6 @@ of a model, see the usage guides on
 
 </Infobox>
 
-<Infobox title="📖 Entity Linking">
-
-To learn more about entity linking in spaCy, and how to **train and update** the
-entity linker predictions, see the usage guides on
-[entity linking](/usage/linguistic-features#entity-linking) and
-[training the entity linker](/usage/training#entity-linker).
-
-</Infobox>
-
 ### Word vectors and similarity {#vectors-similarity model="vectors"}
 
 import Vectors101 from 'usage/101/\_vectors-similarity.md'
diff --git a/website/docs/usage/v2-2.md b/website/docs/usage/v2-2.md
index c429559ad..3b53a3945 100644
--- a/website/docs/usage/v2-2.md
+++ b/website/docs/usage/v2-2.md
@@ -11,10 +11,10 @@ menu:
 spaCy v2.2 features improved statistical models, new pretrained models for
 Norwegian and Lithuanian, better Dutch NER, as well as a new mechanism for
 storing language data that makes the installation about **15&times; smaller** on
-disk. We've also added a new API for **entity linking**, a new class to
-efficiently **serialize annotations**, an improved and 10&times; faster phrase
-matching engine, built-in scoring and **CLI training for text classification**
-and a new command to analyze and **debug training data**. For the full
+disk. We've also added a new class to efficiently **serialize annotations**, an
+improved and **10&times; faster** phrase matching engine, built-in scoring and
+**CLI training for text classification**, a new command to analyze and **debug
+training data**, data augmentation during training and more. For the full
 changelog, see the
 [release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.2.0).
 
@@ -45,36 +45,6 @@ overall. We've also added new core models for [Norwegian](/models/nb) (MIT) and
 
 </Infobox>
 
-### Entity linking API {#entity-linking}
-
-> #### Example
->
-> ```python
-> nlp = spacy.load("my_custom_wikidata_model")
-> doc = nlp("Ada Lovelace was born in London")
-> print([(e.text, e.label_, e.kb_id_) for e in doc.ents])
-> # [('Ada Lovelace', 'PERSON', 'Q7259'), ('London', 'GPE', 'Q84')]
-> ```
-
-Entity linking lets you ground named entities into the "real world". We're
-excited to now provide a built-in API for training entity linking models and
-resolving textual entities to unique identifiers from a knowledge base. The
-annotated KB identifier is accessible as either a hash value or as a string from
-a `Span` or `Token` object. For more details on entity linking in spaCy, check
-out
-[Sofie's talk](https://www.youtube.com/watch?v=PW3RJM8tDGo&list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc&index=6)
-at spaCy IRL 2019.
-
-<Infobox>
-
-**API:** [`EntityLinker`](/api/entitylinker),
-[`KnowledgeBase`](/api/knowledgebase) **Code: **
-[`bin/wiki_entity_linking`](https://github.com/explosion/spaCy/tree/master/bin/wiki_entity_linking)
-**Usage: ** [Entity linking](/usage/linguistic-features#entity-linking),
-[Training an entity linking model](/usage/training#entity-linker)
-
-</Infobox>
-
 ### Serializable lookup table and dictionary API {#lookups}
 
 > #### Example